With policy gradient methods [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, the policy is explicitly parameterized, often as a deep feed-forward neural network, \(\pi =\pi _\theta \) . Here \(\theta \)  is a vector of adjustable network parameters. A simple (and widely used) stochastic policy is constructed as a diagonal Gaussian distribution, where the actions are uncorrelated as all non-diagonal entries of the covariance matrix are zero
\(\begin{aligned}a &= \mu _\theta (s) + \sigma _\theta (s) \odot z \\z &\sim \mathcal {N}(0,I)\end{aligned}\) 
